Introduction to RAG Strategies
Retrieval Augmented Generation (RAG) has become essential for building AI agents that can leverage external knowledge bases effectively. However, with numerous strategies available, choosing the right approach for your use case can be overwhelming. This guide covers 11 key RAG strategies, their implementations, and when to combine them for optimal results.
RAG Fundamentals
At its core, RAG consists of two phases:
- Data Preparation: Documents are chunked into bite-sized pieces, embedded, and stored in a vector database
- Query Process: User queries are embedded and used to retrieve relevant chunks, which are then fed to the LLM as context
The challenge lies in optimizing both the chunking strategies and retrieval methods to maximize relevance while minimizing computational overhead.
1. Re-Ranking
Re-ranking implements a two-step retrieval process that significantly improves result quality.
How it works: First, retrieve a large number of chunks from the vector database. Then use a specialized re-ranker model (often a cross-encoder) to identify the most relevant chunks for the specific query.
Benefits:
- Considers more context without overwhelming the LLM
- Higher precision in chunk selection
- Maintains efficiency despite the two-step process
Trade-offs:
- Slightly higher cost due to additional model call
- Minimal latency increase
Best for: Most RAG implementations as a foundational strategy.
2. Agentic RAG
Agentic RAG gives AI agents the autonomy to choose their search strategy based on the query type.
How it works: The agent can perform semantic search, read entire documents, or combine approaches dynamically based on the query requirements.
Benefits:
- Highly flexible and adaptive
- Can handle diverse query types effectively
- Balances precision and comprehensiveness
Trade-offs:
- Less predictable behavior
- Requires clear instructions for different search modes
- More complex implementation
Best for: Applications needing varied search approaches.
3. Knowledge Graphs
Knowledge graphs combine traditional vector search with graph databases to capture entity relationships.
How it works: Extract entities and relationships from documents using LLMs, then enable both similarity search and relationship-based queries.
Benefits:
- Excellent for interconnected data
- Enables complex relationship queries
- Provides richer context understanding
Trade-offs:
- Slower and more expensive to build
- Requires LLM processing for graph construction
- Higher maintenance complexity
Best for: Domains with rich entity relationships (legal, medical, research).
4. Contextual Retrieval
Developed by Anthropic, this strategy enriches each chunk with document-level context.
How it works: Use an LLM to prepend each chunk with information about how it fits into the broader document structure.
Benefits:
- Significantly improves retrieval relevance
- Maintains document context in chunks
- Research shows substantial performance gains
Trade-offs:
- Expensive due to LLM calls during indexing
- Slower data preparation phase
- Increased storage requirements
Best for: Applications requiring high retrieval accuracy.
5. Query Expansion
Query expansion uses LLMs to enhance user queries before searching.
How it works: Take the original query and generate a more specific, detailed version that will retrieve more relevant chunks.
Benefits:
- Simple to implement
- Improves search precision
- No changes needed to data preparation
Trade-offs:
- Additional LLM call per query
- Increased latency
- Requires careful prompt engineering
Best for: Improving search quality with minimal infrastructure changes.
6. Multi-Query RAG
Instead of expanding one query, generate multiple query variants for comprehensive coverage.
How it works: Use an LLM to create several different phrasings of the original query, then search with all variants in parallel.
Benefits:
- More comprehensive knowledge coverage
- Better handling of diverse query expressions
- Parallel processing maintains reasonable latency
Trade-offs:
- Multiple LLM calls and database queries
- Higher computational cost
- Potential for redundant results
Best for: Ensuring comprehensive information retrieval.
7. Context-Aware Chunking
Focuses on intelligent document splitting that preserves structure and meaning.
How it works: Use embedding models to identify natural boundaries in documents for splitting, rather than fixed character counts.
Benefits:
- Maintains document structure
- Free and fast implementation
- Significantly improves embedding accuracy
Trade-offs:
- More complex than simple character-based chunking
- Requires additional processing during indexing
Best for: Documents with clear structural boundaries (reports, articles, code).
8. Late Chunking
A sophisticated approach that embeds documents before chunking.
How it works: Apply embeddings to the full document first, then chunk the resulting token embeddings.
Benefits:
- Maintains full document context in each chunk
- Leverages longer-context embedding models
- Superior context preservation
Trade-offs:
- Highly complex implementation
- Requires specialized embedding models
- Not widely adopted yet
Best for: Applications where context preservation is critical.
9. Hierarchical RAG
Creates multi-level knowledge representations with parent-child relationships.
How it works: Store chunks at different granularities (paragraphs, sections, documents) with metadata linking them hierarchically.
Benefits:
- Balances precision and context
- Enables flexible retrieval depths
- Powerful for structured documents
Trade-offs:
- Increased complexity
- Additional metadata management
- Similar unpredictability to agentic RAG
Best for: Large document collections needing flexible context levels.
10. Self-Reflective RAG
Implements a self-correcting search loop with quality assessment.
How it works: After initial retrieval, use an LLM to grade the results. If below threshold, refine the search and retry.
Benefits:
- Self-correcting and adaptive
- Improves result quality automatically
- Handles edge cases gracefully
Trade-offs:
- Multiple LLM calls per query
- Potential for increased latency
- Requires quality threshold tuning
Best for: Applications needing high reliability and accuracy.
11. Fine-Tune Embeddings
Customizes embedding models for domain-specific similarity measures.
How it works: Train embedding models on domain-specific datasets to capture relevant similarity patterns.
Benefits:
- 5-10% accuracy improvements
- Can outperform larger generic models
- Enables custom similarity metrics (e.g., sentiment-based)
Trade-offs:
- Requires substantial training data
- Ongoing infrastructure maintenance
- Higher development complexity
Best for: Domain-specific applications with available training data.
Strategic Recommendations
When implementing RAG, focus on combining 3-5 strategies rather than trying to implement all approaches. The most effective starting combination includes:
- Re-ranking - For improved retrieval precision
- Agentic RAG - For search flexibility
- Context-Aware Chunking - For better data preparation
This combination provides a strong foundation that can be extended with additional strategies as needed.
Implementation Considerations
- Database Choice: PostgreSQL with pgvector (as used in Neon) provides excellent performance for most RAG implementations
- Cost Optimization: Balance between retrieval quality and computational overhead
- Monitoring: Track retrieval accuracy and latency to guide optimization efforts
- Hybrid Approaches: Consider combining multiple strategies for complex use cases
Conclusion
RAG strategies offer powerful ways to enhance AI agent capabilities, but success depends on choosing the right combination for your specific use case. Start with the recommended core strategies and iteratively add complexity based on your performance requirements and data characteristics.
Remember that the optimal solution typically involves combining approaches rather than relying on a single strategy. Focus on measuring retrieval accuracy, response quality, and system performance to guide your implementation decisions.
This article was written by Cline, based on content from: https://www.youtube.com/watch?v=tLMViADvSNE